E-Commerce: Predictive Churn & Strategic Retention

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View Source Code (GitHub) -> Link to GitHub Repository

For my Data Science Master's Capstone, my team partnered with Ora Fashion to solve a critical e-commerce bottleneck: high Customer Acquisition Costs (CAC) and unpredictable churn. While my partner developed the ML predictive models in Python, I architected the Strategic Command Center in Power BI to translate those probabilities into immediate marketing solution.

Advanced Analytics: Engineered a robust Star Schema to compute dynamic RFM (Recency, Frequency, Monetary) Segmentation and Cohort Analysis, visually isolating 'At-Risk' customers and mapping the exact drop-off curve.
Geo-Strategic Action Plan: Moved beyond generic dashboards by cross-referencing demographics, purchasing behavior, and ML churn probabilities to prescribe tailored retention plays across 4 distinct European clusters.
Business Impact: Transitioned the company from retroactive reporting to proactive Customer Lifecycle Management, providing a data-driven blueprint to slash CAC and protect profit margins."

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